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Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
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MWE-Aware English Dependency Corpus
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Abstract:
*Introduction* MWE-Aware English Dependency Corpus was developed by the Nara Institute of Science and Technology Computational Linguistics Laboratory and consists of English compound function words annotated in dependency format. The data is derived from OntoNotes Release 5.0 (LDC2013T19). Compound function words are a type of multiword expression (MWE). MWEs are groups of tokens that can be treated as a single semantic or syntactic unit. Doing so facilitates natural language processing tasks such as constituency and dependency parsing. Version 2.0 is available from LDC as MWE-Aware English Dependency Corpus 2.0 (LDC2017T16) *Data* MWE-Aware English Dependency Corpus was derived from the Wall Street Journal portion of OntoNotes Release 5.0. MWEs were identified in OntoNotes' phrase structure trees and each MWE was established as a single subtree. Those phrase structure subtrees were then converted to a dependency structure (the Stanford dependencies) in CoNLL format. The data is split into 1,728 phrase structure trees as *.parse files and a single 14-column tab separated dependency as a *.conll file. Both file types are encoded as UTF-8. *Samples* Please view this sample. *Updates* None at this time.
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URL: https://catalog.ldc.upenn.edu/LDC2017T01
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| 19 |
A Psycholinguistic Model for the Marking of Discourse Relations
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In: Dialogue & Discourse; Vol 8 No 1 (2017); 106-131 ; 2152-9620 (2017)
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Topic-informed neural machine translation
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In: Zhang, Jian, Li, Liangyou orcid:0000-0002-0279-003X , Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2016) Topic-informed neural machine translation. In: 26th International Conference on Computational Linguistics, 13-16 Dec 2016, Osaka, Japan. (2016)
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